Updates

Model and report changes

  1. The definition of deaths has been adapted to include all deaths that occur in individuals who have had lab-confirmed infection within 60 days from the date of their most recent positive test. This definition reflects more realistically the burden of COVID-19.
  2. Using observations of improved survival in hospitalised COVID-19 patients, we have allowed the probability of dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) to gradually change over the course of June 2020, with a decrease being estimated.
  3. The model uses seroprevalence data on the presence of COVID-19 antibodies in blood samples taken by NHSBT to estimate the levels of cumulative infection within the population over time. As, from early June, the NHSBT has been giving a constantly declining prevalence of antibodies, and these data have been curtailed at this point.
  4. The modelling now accounts for a different susceptibility to infection in the under-15s, using information from literature (Viner et al, 2020) suggesting that children less likely to acquire infection when in contact with an infectious individual.
  5. The geographical definition has been changed from the seven NHS regions (map) to the nine regions typically used in government (map). This new spatial definition more appropriately reflects the existing regional heterogeneity.

Updated findings

  1. The current estimate of the daily number of new infections occurring each day across England is 58,800 (40,900–81,800, 95% credible interval).
  2. The daily number of new infections is particularly high in the Midlands regions (7,700 and 9,710 infections per day in the East and West, respectively), London (12,000) and the South-East (7,900). Note that a substantial proportion of these daily infections will be asymptomatic.
  3. We predict that the number of deaths occurring each day is likely to be between 305 and 531 on the 28th of December.
  4. We estimate Rt to be close to 1 in most regions. The probability of Rt exceeding 1 is above 80% in London, East of England (EE), South East (SE) and South West (SW), while it is less than 10% in the North East (NE). We can be certain that Rt is lower than 1 in Yorkshire and Humber (Y&H) and North West (NW).
  5. The growth rate for England is estimated to be 0.00 (-0.01–0.03, 95% credible interval) per day. This means that, nationally, the number of infections has stabilised. However, there is still evidence of continued growth in SE and SW, EE and London, while we confidently estimate we are beyond a second peak in infections in the NW, NE and Y&H.
  6. London, followed by the NW, continues to have the highest attack rate, that is the proportion of the population who have ever been infected, with 20% and 18% respectively. The SW continues to have the lowest attack rate though this has been revised upwards to 6%.
  7. Note that the deaths data used are only very weakly informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to some revision.

Interpretation

The plots of Rt over time continue to show a downward trend, started in early-to-mid-September in the Northern regions and early-October elsewhere, resulting in values below 1 in NE, NW, YH and close to 1 in the West Midlands.

The number of new infections has peaked in the NE NW and YH and plateaued in the East and West Midlands, but continues to increase in the SE, SW, EE and London.

Trends in the number of daily deaths occurring each day follow a similar pattern but with a lag of 2-3 weeks. The combination of these different patterns across regions explain the unusual shape of the estimated trends for deaths.

The lower values of Rt and the decrease in the number of new infections are likely to be resulting from the combination of social distancing interventions, half-term school closures as well as the most recent lockdown. This lock-down seems to have contributed to the continuation of the downwards trends, however this contribution appears quite modest.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

IFR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 -0.01 0.01
East of England 0.02 -0.02 0.04
East Midlands -0.01 -0.03 0.01
London 0.01 -0.01 0.03
North East -0.04 -0.07 -0.01
North West -0.05 -0.08 -0.02
South East 0.01 -0.02 0.03
South West 0.01 -0.01 0.04
West Midlands -0.01 -0.03 0.01
Yorkshire and The Humber -0.05 -0.08 -0.03

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 68.35 NA
East of England NA 45.32 NA
East Midlands 111.75 22.81 NA
London NA 48.12 NA
North East 18.49 9.77 65.20
North West 14.47 8.85 32.18
South East NA 36.42 NA
South West NA 45.94 NA
West Midlands 67.07 22.20 NA
Yorkshire and The Humber 12.72 8.18 23.73

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 589.56 57.27 NA
East of England 41.91 15.80 NA
East Midlands NA 53.35 NA
London 60.00 21.16 NA
North East NA NA NA
North West NA NA NA
South East 109.11 23.12 NA
South West 58.96 18.52 NA
West Midlands NA 94.38 NA
Yorkshire and The Humber NA NA NA

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 -0.01 0.01
East of England 0.02 -0.01 0.05
East Midlands 0.00 -0.02 0.03
London 0.02 -0.01 0.04
North East -0.02 -0.04 0.00
North West -0.04 -0.05 -0.02
South East 0.01 -0.01 0.04
South West 0.02 0.00 0.05
West Midlands 0.00 -0.02 0.02
Yorkshire and The Humber -0.04 -0.05 -0.02

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 497.46 74.06 NA
East of England NA 54.54 NA
East Midlands NA 41.09 NA
London NA 96.84 NA
North East 30.67 15.59 NA
North West 18.64 12.87 40.48
South East NA 95.96 NA
South West NA 189.44 NA
West Midlands NA 40.87 NA
Yorkshire and The Humber 18.98 13.04 37.93

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 109.47 NA
East of England 42.92 15.06 NA
East Midlands 151.55 27.24 NA
London 38.95 16.21 NA
North East NA 2902.06 NA
North West NA NA NA
South East 46.43 18.41 NA
South West 32.07 14.51 NA
West Midlands 294.04 31.27 NA
Yorkshire and The Humber NA NA NA

Infections and deaths

The blue lines is show when interventions have been introduced (lockdown on 23 Mar and the relaxation of measures on 11 May), and the red line shows the date these results were produced (08 Dec).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Deaths incidence

By region

By age

Cumulative deaths

By region

By age

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

\(R_t\)

Copyright © MRC Biostatistics Unit, University of Cambridge